The ability to differentiate between a series of one or more objects comes naturally to human beings. A 5-year old with a set of building blocks can separate the blocks according to size, color, texture, and many other discernible characteristics. Most children can even add more categories to the classification scheme as new qualities appear. For example, as the building blocks age, the surface of the building blocks may fade. If new blocks are introduced to the child, the child can easily tell the difference between the new blocks and the old blocks. Current computer systems, however, find such tasks enormously difficult. Existing systems for classifying objects contained within an image are inherently limited and cannot, for example, effectively identify how many objects of a particular type exist in an image. The limitations of existing technologies become increasingly evident when complex images are to be processed. For example, when the characteristics that distinguish one entity from another are subtle and vary from entity to entity, existing computer systems become unable to accurately classify entities in an image as belonging to a certain type.
There are many uses for an improved system that can reliably quantify entities across multiple sets of image data. For instance, scientists, laboratory technicians, doctors, and other professionals have a need for a technology that enables the extraction of quantitative information from an image. Accurately counting the number of entities in an image requires that the person performing the count understand the various forms and nuances associated with the types of entity being counted. A pathologist may be able to look at a particular red blood cell sample and approximate how many red blood cells are in that sample. A research biologist may need to quantify the number of entities present in a histological brain section for purposes of an experiment, but be prevented from doing so by the lack of time or expertise required to manually perform such an analysis. Similarly, a materials scientist may want to count the number of carbon fibers within a cross section of a structural support but be prevented from doing so due to the large number of carbon fibers in the structural support.
Current systems do not have a mechanism for incorporating the expertise of people skilled at identifying a certain entity type. As a result, there is a need for an image classification system that can incorporate such expertise and give others the opportunity to benefit from it. For instance, while a histologist may have the patience to count a few given entities, he or she will usually do so only to a limited degree due to time and cost. Thus the scientific field has been dominated by illustrating findings with a few select captured images resulting in overly qualitative conclusions. When image classification is utilized to support a particular finding, it is typically done so in areas where the fields are not particularly crowded or where the entities of interest in an image are rarely re-presented. Counting the number of entities in a crowded image has been impractical. Similarly the counting of entities requiring searching over many fields is impractical. There is another key issue however in terms of consistency of entity assignment among viewers, whether they be inexperienced or professional. Entities often have different features and diverse forms despite the fact they belong to the same entity class. In many cases even the professional has their own distinct classification criteria that are not clearly defined, giving rise to inconsistent results across studies. The labor, monotony, and expertise required for the task often precludes investigation into avenues that may have significant merit, but that are exceedingly difficult to perform.
Due to the problems associated with quantifying image data, there is a need for an improved technology that aids the process of obtaining quantitative data from images such as scientific samples. Such a technology has the potential to provide scientists and other users with important insights into the progression of many different diseases as well as the identification of distinguishing features among diseases. Likewise, chemists or materials scientists may discover new processes or improve compounds when aided in the classification and quantification of their unique images.
Some examples of current image quantification techniques and the problems associated with these techniques will now be discussed so as to provide the reader with an understanding of the need for an improved solution. Image Pro Plus, a software package for processing biological images, nicely exemplifies the standard approach to classification. Image Pro Plus™, is an example of a current system that provides a mechanism for counting, measuring, and/or classifying entities in digital images. Image Pro Plus provides the user with several methods for classifying pixels in terms of their colors. Image Pro Plus provides a mechanism for classifying entities in an image based on their morphology, but the system is difficult to use and does not “learn” how to improve its analytical skill over time. To classify the pixels in an image, the Image Pro Plus user must first interact with the application to define different pixel classes. For example, in the “color cube based dialog” Image Pro Plus divides the set of possible pixel colors into a cube, where a color corresponds to a point (r, g, b) in the cube with red, green and blue intensities r, g and b. The user defines as many distinct pixel classes as he/she wishes. For each class, the user uses an eyedropper tool to select the colors he/she wants to include in the class. When all classes have been defined, Image Pro Plus displays an image in which pixels are partitioned into the appropriate pixel classes. If a given color has been included in two different classes, pixels of that color get assigned to whichever class was defined first.
What Image Pro Plus and other current systems lack is the ability to embody the knowledge of the trained histologist within a general tool that can be used to automate the classification of pixels and/or entities across a broad range of images. The importance of such a general tool lies in its potential to standardize the classification of histological structures across an entire biomedical field or subfield (e.g., the subfield focusing on Alzheimer's Disease). In addition, these same issues also hinder classification of image data in other scientific disciplines as well (e.g. materials science, chemistry, etc. . . . ).
Thus, there is a need for a system that improves upon the existing methodologies and systems for classifying image data. Such an improved system will now be described in detail.